Root Cause Analysis Using Data Correlation
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Solution Overview
Problem
Conventional root cause analysis technologies in IT environments are inefficient due to their reliance on time and policies/rules to filter out root cause candidates, which is not scalable for large environments and does not effectively leverage collected data points, leading to excessive time and resource expenditure in isolating service degradation issues.
Innovation Solution
The implementation of a data correlation algorithm, such as the Pearson product-moment correlation coefficient, is applied to reduce the number of comparisons and rank potential root causes, providing a more meaningful and practical list for operators to identify the primary cause of service degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional root cause analysis uses time and policies/rules to filter root cause candidates, then the method is simple to implement, but it is not scalable for large environments and does not leverage collected data points effectively
Solution Approach 1:
The patent replaces the mechanical system of manual rule-based filtering with a mathematical correlation algorithm (Pearson product-moment correlation coefficient) that automatically analyzes data points. This substitution enables the system to handle large environments efficiently while leveraging collected data points, resolving the contradiction between implementation simplicity and scalability.
2Reliability
If conventional root cause analysis performs comprehensive data correlation, then it can identify all potential causes, but the computational overhead is prohibitively expensive and not scalable
Solution Approach 1:
The patent applies partial action by using the correlation algorithm only on a filtered subset of root cause candidates that pass the initial time and rule-based filtering. This partial application of the computationally expensive correlation method reduces energy consumption while still identifying the most likely causes, resolving the contradiction between completeness and computational overhead.
3Measurement precision
If conventional root cause analysis correlates specific alarm with all events in database, then it ensures thorough analysis, but the time to isolate root cause is excessive
Solution Approach 1:
The patent segments the root cause analysis process into multiple stages: first filtering candidates using time-based and rule-based methods, then applying correlation analysis only to the filtered subset. This segmentation maintains thoroughness in identifying potential causes while significantly reducing the time required by eliminating unnecessary correlations.
Data Source
AI summary
Embodiments disclosed herein can significantly optimize a root cause analysis and substantially reduce the overall time needed to isolate the root cause or causes of service degradation in an IT environment. By building on the ability of an abnormality detection algorithm to correlate an alarm with one or more events, embodiments disclosed herein can apply data correlation to data points collected within a specified time window by data metrics involved in the generation of the alarm and the event(s). The level of correlation between the primary metric and the probable cause metrics may be adjusted using the ratio between theoretical data points and actual points. The final Root Cause Analysis score may be modified depending upon the adjusted correlation value and presented for user review through a user interface.